Enhanced Facial Emotion Recognition System Using Deep Convolutional Neural Network

Facial emotion recognition is essential in improving human-computer interaction, yet accurately identifying emotions from real images remains a challenging task. To resolve this issue, we employed a Deep CNN model improve the accuracy of emotion identification. In this research, we evaluated the performance of three deep learning models: MobileNetV3, Yolov8n-cls and a proposed Deep CNN model, using the JAFFE dataset, which includes images reflecting seven different emotions. As assessment measures, we used f1-score, precision, and recall rating the models. MobileNetV2 and Yolovov8n-cls achieved an accuracy of 94% and 67% respectively while the proposed Deep CNN model demonstrated superior performance with an accuracy of 99.56%, highlighting its effectiveness in facial emotion recognition.